Convolutional Symmetric AutoEncoders: enhancing latent stability via differential geometry
G. Li Causi, N. Tonicello, L. Magri, G. Rozza
math.NA, stat.ML
Submitted: 2026-07-01
Comments: 28 pages, 17 figures
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
- Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics
- Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
- Auto-Encoding Variational Bayes
- Physics-Driven Convolutional Autoencoder Approach for CFD Data Compressions
Related papers
- Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm
- A Neural-preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions
- Advanced Linear Algebra with Applications - Part I (Numerical linear algebra for PDEs, machine learning, and data assimilation)
- Operator learning meets inverse problems: A probabilistic perspective
- Second-order consistency for learning chaotic dynamics via randomized Jacobian matching
- Windowed thinning and query complexity for the bouncy particle and Zigzag samplers